Online Program Home
My Program

Abstract Details

Activity Number: 538
Type: Contributed
Date/Time: Wednesday, August 3, 2016 : 10:30 AM to 12:20 PM
Sponsor: Biometrics Section
Abstract #319922 View Presentation
Title: Comparisons of Statistical Methods for Determining Gene Expression Signatures to Predict Prostate Cancer Response
Author(s): Dirk Moore and Qian Dong*
Companies: Rutgers School of Public Health and Celgene
Keywords: prediction ; prostate cancer ; lasso ; random forests ; gradient-boosted machines ; support vector machines

Our purpose is to evaluate and compare the performances of different classification methods for predicting prostate cancer outcomes using gene expression data. Specifically, we develop a systematic statistical strategy for constructing a reliable and precise classifier for predicting cancer outcome. We focus on comparing selected statistical methods to predict binary cancer outcome using gene expression data from a cohort of Swedish prostate cancer patients. The methods we compare are logistic regression, lasso regression, regression trees, random forests, gradient-boosted machines, and support vector machines. We perform extensive simulation studies to compare the performance of the selected classification methods.

Authors who are presenting talks have a * after their name.

Back to the full JSM 2016 program

Copyright © American Statistical Association